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1 Internet Appendix to Stock Market Liquidity and the Business Cycle Randi Næs, Johannes A. Skjeltorp and Bernt Arne Ødegaard This Internet appendix contains additional material to the paper Stock Market Liquidity and the Business Cycle. The appendix contains the following additional material: 1. A Microscope on the Recent Financial Crisis We show the evolution of liquidity measures for the period 2004 to 2008 for the U.S., and 2004 to 2009 for Norway. 2. Liquidity Correlation across Countries We show the correlation of liquidity measures, both across liquidity measures and across countries. 3. Predictability of U.S. Macroeconomy, Alternative Time-Series Liquidity Specifications We rerun the analysis in Tables IV, V, and VII in the paper for two alternative time-series transformations of the ILR and LOT liquidity measures (demeaning and Hodrick-Prescott filtering). 4. Predicting U.S. Macroeconomic Variables with Liquidity, VAR Specifications We rerun the analysis in Table IV in the paper using a VAR (vector auto regression) specification. We report Granger causality tests between all variables in VAR and analyze the impulse response functions (we focus on the response of dgdpr to a shock in dilr) and examine the robustness of the response function to different orderings of the endogenous variables. 5. Additional U.S. Size Results We report estimation results for liquidity measures constructed separately for small and large firms for additional macro variables (due, dconsr, and dinv ). This supplements Table VII in the paper. 6. Additional Model Specifications for the U.S., Excluding Market Liquidity In table IV in the paper, we show the adjusted R 2 for models where we exclude the liquidity variable. We show the estimated models behind these numbers. We also show various alternative model specifications for models excluding market liquidity. 7. Predictability Results and Causality Tests for Norway We report the results for Norway, discussed in Section IV in the paper. I. A Microscope on the Recent Financial Crisis The recent financial crisis is of particular interest for the purposes of this paper, because it has been argued to be a prime example of lack of liquidity leading to a crisis and in turn a real economic recession. To illustrate how stock market liquidity has played out during the crisis in the markets considered in the paper, we provide some time-series plots of the various liquidity measures, starting in 2004, for both the U.S. (figure IA.1) and Norway (figure IA.2). * Citation format: Næs, Randi, Johannes A. Skjeltorp, and Bernt Arne Ødegaard, 201, Internet Appendix to "Stock Market Liquidity and the Business Cycle," Journal of Finance LXVI, , Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the authors of the article. 1

2 Panel A: Relative Spread, quarterly (left) and monthly (right) Panel B: Quarterly Roll (left) and LOT (right) Panel C: ILR, quarterly (left) and monthly (right) Figure IA.1. Liquidity evolution of NYSE in the period 2004 to The figures show time-series of aggregate measures of liquidity at the NYSE in the period 2004 to The measures are calculated for each firm at the NYSE using data for either one month or one quarter. We then calculate (equally weighted) averages of the liquidity measures calculated for individual firms. In panel A we show the relative spread, calculated as quarterly (left) or monthly (right) averages over daily closing spread. In panel B, the Roll (left) and LOT (right) measures are calculated using one quarter of daily returns. In panel C, the ILR measure is calculated using one quarter (left) or one month (right) worth of daily measures. 2

3 Panel A: ILR liquidity measure Panel B: Relative spread Panel C: Monthly turnover Figure IA.2. Liquidity evolution in Norway during the period 2004 to The figures plot the evolution of various liquidity measures at the Oslo Stock Exchange in the period 2004 to The ILR is Amihuds illiquidity ratio, calculated with data for one month, the relative spread is the average over the month of the cross-sectional averages of (end of day) relative spreads. The turnover is the fraction of a stock s outstanding equity traded during a month. 3

4 II. Liquidity Correlation across Countries In the paper we show correlations between liquidity measures by calculating different liquidity measures for the same stock in a given quarter, and use this as the basic observation for calculating the correlation between liquidity measures. An alternative way of calculating a correlation between liquidity measures, which also allows for comparisons across exchanges, is to instead use a cross-sectional liquidity measure for the whole market as the basic observation used to calculate the correlation. In Table IA.I we show such correlations of the aggregate liquidity measures, both within and across exchanges, i.e. the correlations between two time-series of cross-sectional averages. Table IA.I Correlations between Time-series of Average Liquidity Measures The table shows correlations between time-series of average liquidity measures. For each liquidity measure, on each date, we calculate the equally weighted average across all stocks present at that date. The numbers in the table are correlations between the resulting time-series of averages. The time-series used differ. For the U.S., we have LOT, ILR, and Roll for 1947 to The relative spread (RS) for NYSE starts in 1980, the same time as the Norwegian data start. All series stop at the end of US US US US Norway Norway Norway RS LOT ILR Roll RS LOT ILR US LOT 0.66 US ILR US Roll Norway RS Norway LOT Norway ILR Norway Roll III. Predictability of U.S. Macroeconomy, Alternative Time-series Liquidity Specifications In Subsection I.D of the paper we discuss our choices of time-series transformations of ILR and LOT to achieve stationarity, and in the paper we end up using (log) differences for ILR and LOT. But there are alternative ways to achieve stationarity. In this section of the appendix we show two alternative transformations. First we show the results when we demean the ILR and LOT measures using a two-year (backward-looking) moving average. Second we show results using a Hodrick-Prescott filter to detrend ILR and LOT. Note that the series using a Hodrick-Prescott filter can not be used in our out-of-sample forecasting analysis, since it is estimated using future data. The first method, however, only uses data available when the mean is removed, and could be used in forecasting exercises. In the paper we use the first (log) differenced versions of the liquidity variables for both the in-sample and out-of-sample analysis. A. Time demeaned versions of ILR and LOT In the following tables we rerun the analysis reported in tables IV, V and VII in the paper using the time demeaned versions of ILR and LOT to make each series stationary. The demeaning is done by taking the difference between the quarter t realization of the variable and the moving average over the most recent eight quarters. Essentially, we are removing a time-varying trend in the ILR and LOT series. 4

7 Table IA.III Granger Causality Tests - Demeaned ILR and LOT The table shows Granger causality tests between the quarterly real GDP growth (dgdpr) and the demeaned versions of the Amihud Illiquidity ratio (ILR) and the LOT measure. Both ILR and LOT are demeaned relative to their two-year moving average. The test is performed for the whole sample, and different sub-periods. For each measure we first test a null hypothesis that real GDP growth does not Granger cause market illiquidity and then whether market illiquidity does not Granger cause real GDP growth. We report the χ 2 and p-value (in parenthesis) for each test. We choose the optimal lag length for each test based on the Schwartz criterion. For each illiquidity variable the test is performed on the whole sample period (1947q1-2008q4) and the first (1947q1-1977q4) and second (1978q1-2008q4) halves of the sample, and for rolling 20-year subperiods overlapping by 10 years. The first two rows report the number of quarterly observations covered by each sample period and the number of NBER recession periods within each sample. Whole First Second sample half half 20-year subperiods N (observations) NBER recessions Panel A: dilr (demeaned) H 0 : dgdpr dilr χ p-value H 0 : dilr dgdpr χ p-value Panel B: dlot (demeaned) H 0 : dgdpr dlot χ p-value H 0 : dlot dgdpr χ p-value

9 Table IA.V Granger Causality - Size Portfolios (Demeaned LOT and ILR) The table shows the results of Granger causality tests between real GDP growth and the illiquidity of small and large firms for the two different illiquidity proxies. Both ILR and LOT are demeaned relative to their two-year moving average. The first column denote the liquidity variable, columns two and three show the χ 2 and associated p-value from Granger causality tests where the null hypothesis is that GDP growth does not Granger cause the liquidity variables. Similarly, columns four and five show the results when the null hypothesis is that the liquidity variable does not Granger cause GDP growth. Liquidity dgdpr LIQ LIQ dgdpr variable (LIQ) χ 2 p-value χ 2 p-value ILR S 0.00 (0.97) (0.00) ILR L 0.40 (0.53) 1.39 (0.24) LOT S 0.67 (0.72) 6.44 (0.04) LOT L 0.19 (0.91) 5.60 (0.06) 9

12 Table IA.VII Granger Causality Tests - HP Filtered ILR and LOT The table shows Granger causality tests between quarterly real GDP growth (dgdpr) and the Amihud illiquidity ratio (ILR) and LOT measures. We use specifications of ILR and LOT that have been detrended with a Hodrick-Prescott filter. The test is performed for the whole sample, and different subperiods. For each measure we first test the null hypothesis that real GDP growth does not Granger cause market illiquidity and then whether market illiquidity does not Granger cause real GDP growth. We report the χ 2 and p-value (in parentheses) for each test. We choose the optimal lag length for each test based on the Schwartz criterion. For each illiquidity variable the test is performed on the whole sample period (1947q1-2008q4), the first (1947q1-1977q4) and second (1978q1-2008q4) halves of the sample, and for rolling 20-year subperiods overlapping by 10 years. The first two rows report the number of quarterly observations covered by each sample period and the number of NBER recession periods within each sample. Whole First Second sample half half 20-year sub-periods N (observations) NBER recessions Panel A. ILR (HP filtered) H 0 : dgdpr dilr χ p-value H 0 : dilr dgdpr χ p-value Panel B. LOT (HP filtered) H 0 : dgdpr dlot χ p-value H 0 : dlot dgdpr χ p-value

14 Table IA.IX Granger Causality - Size Portfolios (HP Filtered ILR and LOT ) The table shows the results of Granger causality tests between real GDP growth and the illiquidity of small and large firms for the two different illiquidity proxies. We use specifications of ILR and LOT that have been detrended with a Hodrick-Prescott filter. The first column denotes the liquidity variable, columns two and three show the χ 2 and associated p-value from Granger causality tests where the null hypothesis is that GDP growth does not Granger cause the liquidity variables. Similarly, columns four and five show the results when the null hypothesis is that the liquidity variable does not Granger cause GDP growth. Liquidity dgdpr LIQ LIQ dgdpr variable (LIQ) χ 2 p-value χ 2 p-value dilr S 3.08 (0.38) (0.00) dilr L 6.61 (0.09) 1.76 (0.62) dlot S 7.59 (0.02) (0.01) dlot L 3.16 (0.21) 0.58 (0.75) IV. Predicting U.S. Macroeconomic Variables with Liquidity, VAR Specifications Chordia, Sarkar, and Subrahmanyam (2005) argue that returns, volatility and liquidity are endogenous and should be estimated in a system. Thus, to supplement the predictive regressions in Table IV of the paper, we first estimate a VAR with endogenous equity market variables to examine the causal relationships between these variables. In addition, we include equity market turnover. 1 In the second set of VAR models we also include the credit spread (dcred) and term spread (Term) as endogenous variables. In the VAR estimations we use the first (log) differenced versions of ILR and LOT, while Roll is not transformed. A. VAR - only Equity Market Variables In Table IA.X we report the estimation results for a VAR system with dgdpr, er m, Vola, and either dilr (Panel A), dlot (Panel B), or Roll (Panel C). The model is estimated with a one quarter lag for all variables. The number of lags is obtained testing for optimal lag length using the Schwartz criterion. Looking first at the equation for dgdpr, shown in the first row in all panels, the results are very similar to the single-equation predictive regressions in the paper. The dilr, dlot, and Roll measures are all very significant. For the equation for the respective liquidity measures (second row), we find that er m is a strong predictor of both dilr and dlot, although er m does not have any predictive power for Roll. Next, in the equation for er m, no variables enter significantly. In the equation for dturn (stock market turnover), we find that both dgdpr and er m enter significantly in all equations, and in Panel B we also find that dlot is significant in the dturn equation. Finally, in the equation for Vola, we find that lagged market returns er m are significant in the VAR with the Roll measure. In Table IA.XI we test the Granger causality between all the endogenous variables. In the table the null hypothesis is that the row variable does not Granger cause the column variable. For all three liquidity proxies we reject the null that the liquidity measures do not Granger cause dgdpr, while we cannot reject the reverse hypothesis that dgdpr does not Granger cause any of the liquidity variables. While there is no causality from er m to dgdpr in the systems with dilr or Roll, we reject the null in favor of er m (at the 5% level) Granger causing dgdpr in the system with dlot. Interestingly, for all three models we find support for a strong one-way causality from dgdpr to dturn and from er m to dturn. Finally, we also 1 The turnover (Turn) is estimated for each stock as the fraction of the firm s equity capital traded in a given quarter. We then take equally weighted averages for all observations in the same quarter. In the analysis we use (log) differenced turnover, and label it dturn. 14

17 A.1. Impulse Response Functions - only Stock Market Variables To examine more closely the dynamic relationship between market liquidity, stock returns, stock market volatility, turnover, and GDP growth, we compute impulse response functions (IRFs) for GDP growth. By shocking one variable by a one unit standard deviation, the IRF traces the impact on real GDP growth. The (inverse) Cholesky decomposition is used to orthogonalize the residual covariance matrix since the innovations are correlated. Also, it is important to note that the IRFs are sensitive to the ordering of the endogenous variables in the VAR. However, the ordering of the variables does not affect the results in the estimated VAR or the Granger causality tests. Since in the paper we are mainly interested in the information in liquidity about future GDP growth (and to keep the number of figures down) we show only the responses of GDP growth to a shock in dilr. We base the initial ordering of the variables on Chordia, Sarkar, and Subrahmanyam (2005), who argue that information and endowment shocks generally affect prices and liquidity through trading. Therefore, we place stock turnover (dturn) first in the ordering. Chordia, Sarkar, and Subrahmanyam (2005) also note that the ordering of stock returns, stock volatility, and liquidity is unclear. We use their initial ordering of these variables, and for robustness also look at whether different relative orderings of these variables affect the response of dgdpr to shocks in dilr. Finally, since dgdpr at t is not observed by market participants before the following quarter, we always put dgdpr last in the ordering. Thus, our initial ordering of the variables is: dturn, Vola, er m, dilr, and dgdpr (a) dturn, Vola, er m, dilr, dgdpr (b) dturn, dilr, Vola, er m, dgdpr (c) dturn, er m, dilr, Vola, dgdpr (d) dturn, Vola, dilr, er m, dgdpr Figure IA.3. Impulse Response Functions - VAR with dgdpr and Stock Market Variables. The figures show the impulse response functions from a VAR of real GDP growth (dgdpr) and the equity market variables, dilr, er m, Vola, and dturn. The figures show the response of dgdpr to a Cholesky one-standard deviation dilr innovation. The ordering of the variables in the VAR is stated in the caption of each figure. The dotted lines show the +/- two standard deviation uncertainty band. 17

18 From Figure IA.3 we see that the response of dgdpr to a shock in dilr is not greatly affected by the relative ordering of the variables er m, Vola, and dilr. While we keep dturn as the first variable across all four estimations, we also examined the effect of changing the ordering of dturn. The results are insensitive to the placement of dturn in the ordering. B. VAR - All Market Variables In the previous subsection, we estimated a system where we included only stock market variables in addition to dgdpr. In this section we estimate a VAR where we also include the two bond market variables dcred and Term. The main reason for this is that several other studies show that these variables have predictive power for GDP growth and are also related to stock market variables. Since we are mainly interested in adding them as control variables to see whether equity market liquidity contains additional information about future GDP growth, we have chosen not to put any restrictions on the equations for these variables. The first thing to note from Table IA.XII is that in the equation for dgdpr (first row in each panel), all three liquidity variables have significant coefficients of the same size as in the single-equation estimations reported in the paper. With respect to the additional variables, dcred and Term, there are a few interesting results. First, we find that er m is significant and negative in the equation for dcred across all three models. Thus, a lower realized stock market return predicts an increase in the credit spread. Also, we find that the coefficient on Roll is significantly positive in the Term equation, indicating that a high spread costs predicts a larger term spread. In Table IA.XIII we perform Granger causality tests between all the variables in the VAR. The results are very similar to those in the previous section; however, there are a few interesting additional results. In particular, we see that there is causality going from er m to dcred, and also from Roll to Term. Table IA.XII Vector Autoregression - All Market Variables The table shows the results from estimating a VAR with endogenous variables dgdpr, er m, dturn, Vola, dcred, Term and market liquidity proxied either by dilr (Panel A), dlot (Panel B), or the Roll measure (Panel C). dilr and dlot are first (log) differences. The VAR is estimated with a lag of one quarter and a constant term. We choose the optimal number of lags based on the Schwartz criterion. Numbers in parentheses are t-values. Panel A: ILR liquidity measure variable Const. dgdpr (-1) dilr (-1) er m (-1) dturn (-1) Vola (-1) dcred (-1) Term (-1) adj.r 2 dgdpr (4.68) (4.67) (-2.85) (1.74) (-1.53) (0.39) (-1.72) (1.10) dilr (0.50) (1.87) (-2.60) (-6.05) (-0.15) (0.73) (0.60) (-1.67) er m (0.71) (-0.02) (1.34) (1.52) (0.82) (0.72) (-0.14) (1.32) dturn (0.06) (-2.59) (0.84) (2.91) (-1.87) (0.81) (1.40) (1.96) Vola (0.84) (0.00) (-0.21) (-1.81) (-0.88) (-1.40) (0.70) (-0.15) dcred (2.37) (-1.28) (1.45) (-2.49) (1.54) (0.24) (-2.55) (-1.20) Term (4.04) (-1.14) (-0.93) (-1.83) (0.50) (-0.01) (1.50) (18.95) 18

21 B.1. Impulse Response Functions - All Market variables In Figure IA.3 we examined the IRFs in a system with only stock market variables. In Table IA.XII we estimated a full unrestricted VAR where we also added the credit spread (dcred) and the term spread (Term) as control variables, since these have been shown to contain important information about future economic growth. In Figure IA.4 we perform a similar analysis as in Figure IA.3, but now also include the two non-equity market variables dcred and Term. With respect to the ordering of the variables we base our main ordering on Chordia, Sarkar, and Subrahmanyam (2005) who in their initial ordering put bond market variables before stock market variables. Although we examine different types of variables than Chordia et al; we use their general ordering as our base case, and test how sensitive the response of dgdpr to a shock in dilr is to a change in the ordering of the variables. The initial ordering we use is: dcred, Term, dturn, Vola, er m, dilr, and dgdpr. In part (a) of Figure IA.4 we show the IRF for dgdpr from a shock in dilr in the base ordering case, in part (b) we move the bond market variables after the stock market variables, keeping the relative ordering of the stock market variables fixed as in (a), in part (c) we order the bond market variables first again and put dilr between Vola and er m while dturn is kept fixed as the first of the stock market variables, and in part (d) we put dilr after dturn, but before Vola and er m. While we could have tried several other ordering schemes, we believe the selected orderings should detect whether the response function of dgdpr to a shock in dilr is sensitive to the ordering of the variables in the system (a) dcred, Term, dturn, Vola, er m, dilr, dgdpr (b) dturn, Vola, er m, dilr, dcred, Term, dgdpr (c) dcred, Term, dturn, Vola, dilr, er m, dgdpr (d) dcred, Term, dturn, dilr, Vola, er m, dgdpr Figure IA.4. Impulse response functions - VAR with dgdpr and all market variables. The figures show the impulse response functions from a VAR of real GDP growth (dgdpr) and the equity market variables, dilr, er m, Vola and dturn and the bond market variables dcred and Term. The figures show the response of dgdpr to a Cholesky one standard deviation dilr innovation. The ordering of the variables in the VAR is stated in the caption of each figure. The dotted lines show the +/- two standard deviation uncertainty band. 21

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